Abstract
The combination of Petri nets (PNs) as an analysis tool for discrete-event dynamic systems and artificial intelligence heuristic search has been shown to be a promising way to solve flexible manufacturing systems (FMS) scheduling problems. However, the NP hard nature of the problem obscures the PN capability of reasoning about the behavior of the system. In this paper, two techniques to alleviate this drawback are presented: a systematic method to avoid the generation of futile paths within the search graph and a novel hybrid stage-search algorithm. The new algorithm is based on the application of A* guided by a PN-based heuristic within a limited local search frame. An optimization policy is applied to maintain, under evaluation, only the most promising paths. For each system state, the algorithm is able to decide whether an enabled operation should be applied and to maintain this decision until new information forces reconsideration. This eliminates permutation paths and useless scheduling sequences. Experimental results show that the algorithm's cost does not grow exponentially with the size of the problem. Comparison with previous work is given to show the superiority of our approach and the potential of PN-based heuristic search.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
